用自适应图融合模型分析迷幻药对脑连接的影响。
Brain-MGF: Multimodal Graph Fusion Network for EEG-fMRI Brain Connectivity Analysis Under Psilocybin
- 构建多模态图网络，用部分相关性建边、皮尔逊特征节点。
- 融合后在冥想状态达74.0%准确率，静息态ROC-AUC达85.8%。
- 适合研究迷幻药物神经机制的计算神经科学工作者。
致幻剂如裸盖菇素会重塑大脑大尺度连接，但其在电生理（脑电图，EEG）与血流动力学（功能磁共振成像，fMRI）网络中的表现仍不清晰。本文提出Brain-MGF，一种用于联合EEG-fMRI连接分析的多模态图融合网络。每种模态均构建基于偏相关边与皮尔逊谱特征节点的图，并通过图卷积学习个体嵌入。自适应软最大门控融合不同模态，赋予样本特异性权重以捕捉上下文依赖贡献。基于全球最大的单中心裸盖菇素数据集PsiConnect，Brain-MGF在冥想与静息状态下区分了服用与未服用裸盖菇素的条件。融合策略优于单模态及非自适应变体，在冥想任务中达到74.0%准确率与76.5% F1分数，在静息态达76.0%准确率与85.8% ROC-AUC。UMAP可视化显示融合嵌入具有更清晰的类别分离。结果表明，自适应图融合能有效整合互补的EEG-fMRI信息，为刻画裸盖菇素诱导的大尺度神经组织改变提供可解释框架。
原文摘要 · Abstract (English)
Psychedelics, such as psilocybin, reorganise large-scale brain connectivity, yet how these changes are reflected across electrophysiological (electroencephalogram, EEG) and haemodynamic (functional magnetic resonance imaging, fMRI) networks remains unclear. We present Brain-MGF, a multimodal graph fusion network for joint EEG-fMRI connectivity analysis. For each modality, we construct graphs with partial-correlation edges and Pearson-profile node features, and learn subject-level embeddings via graph convolution. An adaptive softmax gate then fuses modalities with sample-specific weights to capture context-dependent contributions. Using the world's largest single-site psilocybin dataset, PsiConnect, Brain-MGF distinguishes psilocybin from no-psilocybin conditions in meditation and rest. Fusion improves over unimodal and non-adaptive variants, achieving 74.0% accuracy and 76.5% F1 score on meditation, and 76.0% accuracy with 85.8% ROC-AUC on rest. UMAP visualisations reveal clearer class separation for fused embeddings. These results indicate that adaptive graph fusion effectively integrates complementary EEG-fMRI information, providing an interpretable framework for characterising psilocybin-induced alterations in large-scale neural organisation.
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